The State of Global Terrorism

An In-Depth Analysis of Trends and Threats

Terrorism has been a constant hindrance on mankind’s journey to achieve global peace and prosperity. From hostage situations and hijackings to mass shootings and bombings, terrorist attacks have a profound impact on both the victims and the larger society; they cause physical harm and loss of life, as well as emotional trauma and psychological distress. Needless to say, they can have long-lasting socio-economic consequences, disrupting trade and commerce, causing job losses, and decreasing investor confidence.

As the frequency of terrorist attacks are increasing at a rate faster than ever, it is crucial to understand them and their trends and patterns. In this blog post, I will be examining various aspects of terrorism including regions, targets, methods and motives using three open-source datasets: Global Terrorism Database, which contains information on over 180,000 global terrorist attacks from 1970 to 2017; World GDP dataset, which includes the GDP per Capita of different countries from 1960 to 2021; and the World Population dataset, which provides the data on fertility rate and net migration of each countries from 1955 to 2020. I hope this will shed some light on this phenomenon of global terrorism and will equip us better to combat them in the future. So lets roll up our sleeves and demystify the data from the world of global terrorism.

Code

import pandas as pd
import numpy as np
import plotly.express as px
import nltk
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn import neighbors
import tensorflow as tf
from PIL import Image
from tensorflow.keras.models import Sequential
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, Flatten, LSTM, SimpleRNN
from tensorflow.keras.layers import Bidirectional, GRU, UpSampling1D
import plotly.express as px
from sklearn.preprocessing import LabelEncoder
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.lines import Line2D
import matplotlib.patches as mpatches
import time
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

df_attacks = pd.read_csv("../data/globalterrorismdb_0718dist.csv", encoding="ISO-8859-1", low_memory=False)
df_attacks.head()
df_attacks = df_attacks[['eventid','iyear', 'imonth', 'iday', 'country_txt', 'region_txt', 
'provstate', 'city', 'latitude', 'longitude', 'suicide', 'attacktype1_txt', 'targtype1_txt', 
'gname', 'motive', 'weaptype1_txt', 'nkill']]
df_attacks.rename(columns={"eventid": "Event ID", "iyear": "Year", "imonth": "Month", 
"country_txt": "Country", "region_txt": "Region", "provstate": "Province/State", "city": "City", "latitude": "Latitude", 
"longitude": "Longitude", "suicide": "Suicide", "attacktype1_txt": "Attack Type",
"targtype1_txt": "Target Type", "gname": "Terrorist Group", "motive": "Motive", 
"weaptype1_txt": "Weapon Type", "nkill": "Casualties"}, inplace=True)

df_population = pd.read_csv("../data/population.csv")
df_population = df_population[["Country","Year", "Migrants(net)", "FertilityRate"]]
df_population.rename(columns= {"FertilityRate": "Fertility Rate", "Migrants(net)": "Migrants (net)"}, inplace=True)

df_gdp = pd.read_csv("../data/world_country_gdp_usd.csv")
df_gdp = df_gdp[['Country Name','year', 'GDP_USD']]
df_gdp.rename(columns= {"Country Name": "Country", "year": "Year", "GDP_USD":"GDP (in USD)", "GDP_per_capita_USD": "GDP (per capita)"}, inplace=True)

df_us_population = pd.read_csv("../data/us_population.csv")
df_us_population = df_us_population[["state", "pop2022"]]
df_us_population.rename(columns= {"state": "State", "pop2022": "Population"}, inplace=True) 

fig = px.scatter_geo(df_attacks, lon="Longitude", lat="Latitude", animation_frame="Year", color="Region",
                     projection="equirectangular", animation_group="Year", title="Terrorist Attacks (1970 - 2017)")
fig.update_layout(title_x=0.44)
fig.show()

Figure 1: Global Terrorist Attacks

Code

us_states = np.asarray(['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'DC', 'FL', 'GA',
                        'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA',
                        'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', 'NM', 'NY',
                        'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX',
                        'UT', 'VT', 'VA', 'WA', 'WV', 'WI', 'WY'])
us_state_to_abbrev = {
    "Alabama": "AL",
    "Alaska": "AK",
    "Arizona": "AZ",
    "Arkansas": "AR",
    "California": "CA",
    "Colorado": "CO",
    "Connecticut": "CT",
    "Delaware": "DE",
    "Florida": "FL",
    "Georgia": "GA",
    "Hawaii": "HI",
    "Idaho": "ID",
    "Illinois": "IL",
    "Indiana": "IN",
    "Iowa": "IA",
    "Kansas": "KS",
    "Kentucky": "KY",
    "Louisiana": "LA",
    "Maine": "ME",
    "Maryland": "MD",
    "Massachusetts": "MA",
    "Michigan": "MI",
    "Minnesota": "MN",
    "Mississippi": "MS",
    "Missouri": "MO",
    "Montana": "MT",
    "Nebraska": "NE",
    "Nevada": "NV",
    "New Hampshire": "NH",
    "New Jersey": "NJ",
    "New Mexico": "NM",
    "New York": "NY",
    "North Carolina": "NC",
    "North Dakota": "ND",
    "Ohio": "OH",
    "Oklahoma": "OK",
    "Oregon": "OR",
    "Pennsylvania": "PA",
    "Rhode Island": "RI",
    "South Carolina": "SC",
    "South Dakota": "SD",
    "Tennessee": "TN",
    "Texas": "TX",
    "Utah": "UT",
    "Vermont": "VT",
    "Virginia": "VA",
    "Washington": "WA",
    "West Virginia": "WV",
    "Wisconsin": "WI",
    "Wyoming": "WY",
    "District of Columbia": "DC",
    "American Samoa": "AS",
    "Guam": "GU",
    "Northern Mariana Islands": "MP",
    "Puerto Rico": "PR",
    "United States Minor Outlying Islands": "UM",
    "U.S. Virgin Islands": "VI",
}
df_attacks_us = df_attacks[df_attacks["Country"] == "United States"] 
df_attacks_us = pd.DataFrame(df_attacks_us.groupby("Province/State")["Event ID"].count())
df_attacks_us = df_attacks_us.reset_index()
df_attacks_us.rename(columns={"Province/State": "State", "Event ID": "Number of Terrorist Attacks"}, inplace=True)
df_attacks_us = df_attacks_us[df_attacks_us["State"] != "Unknown"]
df_attacks_us["State Code"] = df_attacks_us["State"].apply(lambda x: us_state_to_abbrev[x])
def scale_column(df, column, minVal=float('-inf'), maxVal=float('inf')):
    if minVal == float('-inf'):
        minVal = min(df[column])
    if maxVal == float('inf'):
        maxVal = max(df[column])
    res = []
    for val in df[column]:
        res.append((val - minVal) / (maxVal - minVal))
    return res

df_us_population.head()
df_attacks_us = df_attacks_us.merge(df_us_population[['State', 'Population']])
df_attacks_us["Number of Terrorist Attacks (Standardised)"] = df_attacks_us["Number of Terrorist Attacks"] / df_attacks_us["Population"]
tempVal = scale_column(df_attacks_us, "Number of Terrorist Attacks (Standardised)")
df_attacks_us["Number of Terrorist Attacks (Standardised)"] = tempVal
df_attacks_us = df_attacks_us.sort_values(by="Number of Terrorist Attacks (Standardised)", ascending=False)
fig = px.choropleth(df_attacks_us, locations='State Code', color='Number of Terrorist Attacks (Standardised)',
                    color_continuous_scale="Viridis",
                    locationmode="USA-states", 
                    scope="usa",
                    labels={'Number of Terrorist Attacks (Standardised)':'No. of Terrorist Attacks'},
                    title="Terrorist Attacks in the US (1970-2017)")
fig.update_layout(title_x=0.44)
fig.update_layout( legend = {"xanchor": "right", "x": -0, "y":1.9})
fig.update_layout(height=500, width=780)
fig.show()

Figure 2: Terrorist Attacks in the US

Code

stpwrd = nltk.corpus.stopwords.words('english')
extended_list = ["specific",  "motive", "unknown", "Unknown", "incident", "claimed", "responsibility", "however", "unaffiliated", "individual", "identified", "killed", "stated", "anti", "attacks", "protest", "carried", "attack", "trend", "larger", "may", "part", "following", "community", "sources", "violence", "targeting", "noted", "posited", "suspected", "targeting", "members", "noted", "targeted", "also", "assailant", "perpetrator", "meant", "bring attention", "practice", "perpetrator", "assailant", "meant", "bring", "attention"]

stpwrd.extend(extended_list)


df_attacks_us = df_attacks[df_attacks["Country"] == "United States"]
df_attacks_us = df_attacks_us[["Year", "Motive"]]
df_attacks_us = df_attacks_us.dropna()

temp_df = df_attacks_us[(df_attacks_us["Year"] >= 1970) & (df_attacks_us["Year"] < (2000))]
motive = list(temp_df["Motive"].values)
motive = " ".join(motive)

wordcloud = WordCloud(width=1000, height=800,
                background_color ='white',
                stopwords=stpwrd,
                color_func=lambda *args, **kwargs: "green",
                min_font_size = 10).generate(motive)
plt.figure(figsize = (12, 12), facecolor = None) 
plt.imshow(wordcloud) 
plt.axis("off")
plt.tight_layout(pad = 2)
plt.title("Attack Motives (" + str(1970) + " - " + str(2000) + ")", fontdict={'fontsize': 36})
plt.show()

stpwrd = nltk.corpus.stopwords.words('english')
stpwrd.extend(extended_list)


df_attacks_us = df_attacks[df_attacks["Country"] == "United States"]
df_attacks_us = df_attacks_us[["Year", "Motive"]]
df_attacks_us = df_attacks_us.dropna()

temp_df = df_attacks_us[(df_attacks_us["Year"] >= 2000) & (df_attacks_us["Year"] <= (2017))]
motive = list(temp_df["Motive"].values)
motive = " ".join(motive)
from wordcloud import WordCloud
import matplotlib.pyplot as plt

wordcloud = WordCloud(width=1000, height=800,
                background_color ='white', 
                stopwords=stpwrd,
                color_func=lambda *args, **kwargs: "purple",
                min_font_size = 10).generate(motive)
plt.figure(figsize = (12, 12), facecolor = None) 
plt.imshow(wordcloud) 
plt.axis("off")
plt.tight_layout(pad = 2)
plt.title("Attack Motives (" + str(2000) + " - " + str(2017) + ")", fontdict={'fontsize': 36})
plt.show()

(a) 1970-2000

(b) 2000-2017

Figure 3: Attack Motives

Code

yearly_freq = pd.DataFrame(df_attacks.groupby("Year")["Event ID"].count()).reset_index()
yearly_freq.rename(columns={"Event ID": "Number of Terrorist Attacks"}, inplace=True)
fig = px.bar(yearly_freq, x=yearly_freq["Year"], y=yearly_freq["Number of Terrorist Attacks"], title="Frequency of Terrorist Attacks (1970-2017)")
fig.update_layout(title_x=0.5)
fig.update_layout(height=400)
fig.update_layout({
    'plot_bgcolor': 'rgba(0,0,0,0)',
    'paper_bgcolor': 'rgba(0,0,0,0)'
})
fig.show()

Figure 4: Frequency of Terrorist Attacks

Code

region_freq = pd.DataFrame(df_attacks.groupby(["Region", "Attack Type"])["Event ID"].count()).reset_index()
region_freq.rename(columns={"Event ID": "Number of Terrorist Attacks"}, inplace=True)
region_freq = region_freq.sort_values(by="Number of Terrorist Attacks", ascending=False)
region_freq['Attack Type'] = region_freq['Attack Type'].replace(['Bombing/Explosion', 'Hostage Taking (Kidnapping)', 'Facility/Infrastructure Attack', 'Hostage Taking (Barricade Incident)'], ['Bombing', 'Hostage', 'Facility Attack', 'Hostage (Barr.)'])
fig = px.bar(region_freq, x=region_freq["Region"], y=region_freq["Number of Terrorist Attacks"], color="Attack Type", height=400, title="Terrorist Attacks in Different Regions", barmode="relative")
fig.update_layout(title_x=0.5)
fig.update_layout(height=500)
fig.update_layout({
    'plot_bgcolor': 'rgba(0,0,0,0)',
    'paper_bgcolor': 'rgba(0,0,0,0)'
})
fig.show()

Figure 5: Terrorist Attacks in different Regions

Code

df_countries_casualties = pd.DataFrame(df_attacks.groupby(["Country"])["Casualties"].sum().reset_index())
df_countries_terrorist_count = pd.DataFrame(df_attacks.groupby(["Country"])["Event ID"].count().reset_index())
df_countries_terrorist_count.rename(columns={"Event ID": "Number of Terrorist Attacks"}, inplace=True)
df_merged_casualties_count = df_countries_casualties.merge(df_countries_terrorist_count[["Country", "Number of Terrorist Attacks"]])
df_iso_codes = px.data.gapminder()[["country", "iso_alpha"]]
df_iso_codes.rename(columns={"country": "Country", "iso_alpha": "Country Code"}, inplace=True)
df_iso_codes.drop_duplicates(inplace=True)
df_iso_codes = df_iso_codes.reset_index()
df_iso_codes.drop(["index"], axis=1, inplace=True)
df_countries_terrorist_count = df_countries_terrorist_count.merge(df_iso_codes[['Country', 'Country Code']])
fig = px.choropleth(df_countries_terrorist_count, locations="Country Code",
                    color="Number of Terrorist Attacks",
                    hover_name="Country",
                    color_continuous_scale=px.colors.sequential.Plasma,
                    title="Terrorist Attacks (1970 - 2017)")
fig.update_layout(title_x=0.44)
fig.update_layout(height=500, width=880)
fig.show()

Figure 6: Countries with the Highest Number of Attacks

Code

TOP_N = 11
target_freq = pd.DataFrame(df_attacks.groupby("Target Type")["Event ID"].count()).reset_index()
target_freq.rename(columns={"Event ID": "Number of Terrorist Attacks"}, inplace=True)
rem_freq = target_freq.sort_values(by="Number of Terrorist Attacks", ascending=False)[TOP_N:]
target_freq = target_freq.sort_values(by="Number of Terrorist Attacks", ascending=False)[:TOP_N]
target_freq = target_freq[target_freq['Target Type'] != "Unknown"]
fig = px.bar(target_freq, x='Target Type', y='Number of Terrorist Attacks', title="Common Targets of Terrorist Attacks")
fig.update_layout(title_x=0.5)
fig.update_layout(height=500)
fig.update_layout({
    'plot_bgcolor': 'rgba(0,0,0,0)',
    'paper_bgcolor': 'rgba(0,0,0,0)'
})
fig.show()

Figure 7: Common Targets of Terrorist attacks

Code

groupwise_casualty_freq = pd.DataFrame(df_attacks.groupby("Terrorist Group")["Casualties"].sum()).reset_index()
groupwise_casualty_freq = groupwise_casualty_freq.sort_values(by="Casualties", ascending=False)[:16]
notorious_groups = list(groupwise_casualty_freq["Terrorist Group"])
df_notorious_groups = df_attacks[df_attacks["Terrorist Group"].isin(notorious_groups)]
df_notorious_groups = pd.DataFrame(df_notorious_groups.groupby(["Terrorist Group", "Year"])["Casualties"].sum().reset_index())
df_notorious_groups["Terrorist Group"] = df_notorious_groups["Terrorist Group"].replace(["Farabundo Marti National Liberation Front (FMLN)", "Islamic State of Iraq and the Levant (ISIL)", "Kurdistan Workers' Party (PKK)", "Liberation Tigers of Tamil Eelam (LTTE)", "New People's Army (NPA)", "Nicaraguan Democratic Force (FDN)", "Revolutionary Armed Forces of Colombia (FARC)", "Shining Path (SL)", "Tehrik-i-Taliban Pakistan (TTP)"], ["Farbundo Liberation", "ISIL", "Kurdistan W.", "Tamil Tigers", "New People's Army", "Nicaraguan Force", "Colombian Force", "Shining Path", "Taliban Pakistan"])
fig = px.line(df_notorious_groups, x="Year", y="Casualties", color="Terrorist Group", title='Attacks by different Terrorist Groups')
fig.update_layout(title_x=0.5)
fig.update_layout(height=500)
fig.update_layout({
    'plot_bgcolor': 'rgba(0,0,0,0)',
    'paper_bgcolor': 'rgba(0,0,0,0)'
})
fig.show()

Figure 8: Attacks by different Terrorist Groups

Code

def map_region(country):
    region = list(df_attacks[df_attacks["Country"] == country]["Region"])[0]
    return region

country_freq = pd.DataFrame(df_attacks.groupby("Country")["Event ID"].count()).reset_index()
country_freq.rename(columns={"Event ID": "Number of Terrorist Attacks"}, inplace=True)
country_freq = country_freq.sort_values(by="Number of Terrorist Attacks", ascending=False)[:10]
country_freq["Region"] = country_freq["Country"].apply(map_region)
top_five_countries = list(country_freq["Country"].values)[:5]
country_freq_year = pd.DataFrame(df_attacks.groupby(["Year", "Country"])["Event ID"].count().reset_index())
country_freq_year = country_freq_year[country_freq_year["Country"].isin(top_five_countries)]
country_freq_year.rename(columns={"Event ID": "Number of Terrorist Attacks"}, inplace=True)

df_terrorist_gdp = df_gdp[(df_gdp["Country"].isin(top_five_countries)) & ((df_gdp["Year"] >= 1970) & (df_gdp["Year"] <= 2017))]
df_all_gdp = df_gdp[((df_gdp["Year"] >= 1970) & (df_gdp["Year"] <= 2017))]
df_all_gdp = df_all_gdp.dropna()
df_all_gdp = pd.DataFrame(df_all_gdp.groupby("Year").mean().reset_index())
df_all_gdp.rename(columns={"GDP (in USD)": "World"}, inplace=True)
colorList = list(px.colors.qualitative.T10)
if colorList[0] != "black":
    colorList.insert(0, "black")
for country in top_five_countries:
    temp_gdp = df_terrorist_gdp[df_terrorist_gdp["Country"] == country]
    df_all_gdp[country] = list(temp_gdp["GDP (in USD)"])
fig = px.line(df_all_gdp, x='Year', y=df_all_gdp.columns[1:], title="GDP of Terrorist-prone Countries", color_discrete_sequence=colorList, labels={
                     "value": "GDP (in USD)",
                     "variable": ""
                 })
fig.update_layout({
    'plot_bgcolor': 'rgba(0,0,0,0)',
    'paper_bgcolor': 'rgba(0,0,0,0)'
})
fig.update_layout(title_x=0.5)
fig.update_layout(height=400, width=800)
fig.show()


df_all_fertility = df_population[(df_population["Year"] >= 1970) & (df_population["Year"] <= 2017)]
df_terrorist_fertility = df_population[(df_population["Country"].isin(top_five_countries)) & ((df_population["Year"] >= 1970) & (df_population["Year"] <= 2017))]
df_all_fertility = df_all_fertility.dropna()
df_all_fertility = df_all_fertility.drop(['Migrants (net)'], axis=1)
df_all_fertility = pd.DataFrame(df_all_fertility.groupby("Year").mean().reset_index())
df_all_fertility.rename(columns={"Fertility Rate": "World"}, inplace=True)
for country in top_five_countries:
    temp_fertility = df_terrorist_fertility[df_terrorist_fertility["Country"] == country]
    df_all_fertility[country] = list(temp_fertility["Fertility Rate"])
fig = px.line(df_all_fertility, x='Year', y=df_all_fertility.columns[1:], title="Fertility Rate of Terrorist-prone Countries", color_discrete_sequence=colorList, labels={
                     "value": "Fertility Rate",
                     "variable": ""
                 })
fig.update_layout({
    'plot_bgcolor': 'rgba(0,0,0,0)',
    'paper_bgcolor': 'rgba(0,0,0,0)'
})
fig.update_layout(title_x=0.5)
fig.update_layout(height=400, width=800)
fig.show()

(a) GDP

(b) Fertility Rate

Figure 9: Socio-economic Aspects of Terrorist-prone Countries

Code

try:
    del df_attacks["Event ID"]
    del df_attacks["Motive"]
    del df_attacks["Latitude"]
    del df_attacks["Longitude"]
except:
    print("Some of the columns are not present")
df_attacks = df_attacks.dropna()
df_attacks[['Country', 'Region', 'Province/State', 'City', 'Attack Type', 'Target Type', 'Terrorist Group', 'Weapon Type']] = df_attacks[['Country', 'Region', 'Province/State', 'City', 'Attack Type', 'Target Type', 'Terrorist Group', 'Weapon Type']].apply(LabelEncoder().fit_transform)

y = df_attacks["Casualties"]
X = df_attacks.drop(['Casualties'], axis=1)

# Split the data into train (70%), validation (15%), and test (15%) sets
X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.15, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_trainval, y_trainval, test_size=0.20, random_state=42)

scaler = RobustScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
X_val = scaler.fit_transform(X_val)

def create_bilstm():
    model = Sequential()
    model.add(Bidirectional(LSTM(128, activation='relu', input_shape=(12,1), return_sequences=True)))
    model.add(Dropout(0.2))
    model.add(Bidirectional(LSTM(64, activation='relu')))
    model.add(Dropout(0.2))
    model.add(Dense(32, activation='relu'))
    model.add(Dense(1))
    return model

def create_ffnn():
    model = Sequential()
    model.add(Dense(128, activation='relu', input_shape=(12,)))
    model.add(Dropout(0.3))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(32, activation='sigmoid'))
    model.add(Dense(16, activation='tanh'))
    model.add(Dense(1))
    return model

def create_cnn():
    model = Sequential()
    model.add(Conv1D(32, 3, activation='relu', input_shape=(12,1)))
    model.add(MaxPooling1D(2))
    model.add(Conv1D(64, 3, activation='relu'))
    model.add(MaxPooling1D(2))
    model.add(Flatten())
    model.add(Dense(64, activation='relu'))
    model.add(Dense(1))
    return model

def create_gru():
    model = Sequential()
    model.add(GRU(64, activation='tanh', input_shape=(12,1)))
    model.add(Dropout(0.2))
    model.add(Dense(32, activation='tanh'))
    model.add(Dropout(0.2))
    model.add(Dense(1, activation='linear'))
    return model

result = []

dlModels = {"Feed Forward NN": create_ffnn(), "CNN": create_cnn(), "GRU": create_gru(), "Bi-LSTM": create_bilstm()}

X_train_new = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
X_val_new = X_val.reshape(X_val.shape[0], X_val.shape[1], 1)
X_test_new = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)

for name, model in dlModels.items():
    start_time = time.time()
    model.compile(optimizer='adam', loss='mse')
    if name == "Bi-LSTM":
        model.fit(X_train_new, y_train, epochs=20, batch_size=128, validation_data=(X_val_new, y_val))
        y_pred = model.predict(X_test_new)
    else:
        model.fit(X_train, y_train, epochs=20, batch_size=128, validation_data=(X_val, y_val))
        y_pred = model.predict(X_test)
    result.append([name, round(np.sqrt(mean_squared_error(y_test, y_pred)), 2), round(time.time() - start_time, 2)])


mlModels = {"Random Forest": RandomForestRegressor(), "K Neighbors": neighbors.KNeighborsRegressor(), "Decision Trees": DecisionTreeRegressor()}
for name, model in mlModels.items():
    start_time = time.time()
    model.fit(X_train, y_train)
    pred = model.predict(X_test)
    result.append([name, round(np.sqrt(mean_squared_error(y_test, pred)), 2), round(time.time() - start_time, 2)])

pd.options.display.float_format = '{:.2f}'.format
result_df = pd.DataFrame(result, columns=["Model", "Root Mean Squared Error", "Time (in seconds)"])
result_df.to_csv("./results.csv")  

Code

result_df = pd.read_csv("../results/results.csv")
result_df = result_df.sort_values(by=['Root Mean Squared Error'])
matplotlib.rc_file_defaults()
ax1 = sns.set_style(style=None, rc=None)

fig, ax1 = plt.subplots(figsize=(12,6))
colors = ["#5D3FD3", "#5D3FD3", "#5D3FD3","#5D3FD3", "#0096FF", "#0096FF", "#0096FF"]
sns.barplot(data = result_df, x='Model', y='Root Mean Squared Error', alpha=0.5, ax=ax1, palette=colors)
ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=12)
ax1.set_xlabel("Models", fontsize=14)
ax1.set_ylabel("Root Mean Squared Error", fontsize=14)
ax1.set_title("Efficiency of Models", fontsize=16)
ax2 = ax1.twinx()
ax2.set_ylabel("Time (in seconds)", fontsize=14)
dl = mpatches.Patch(color="#5D3FD3")
ml = mpatches.Patch(color="#0096FF")
custom_line = [Line2D([0], [0], color='#0096FF', lw=2), dl, ml]
leg = plt.legend(custom_line, ["Time", "DL Models", "ML Models"], loc="upper left")
for index, lh in enumerate(leg.legendHandles): 
    if index > 0:
        lh.set_alpha(0.5)
sns.lineplot(data = list(result_df["Time (in seconds)"]), marker='o', ax=ax2, color='#0096FF')
plt.show()

Figure 10: Efficiency of Models

Welcome to my super SUPER awesome blog post! \[x^2 = 1\]

Quarto is cool

This section was copy/pasted from various parts of the Quarto website.

Note

Note that there are five types of callouts, including: note, tip, warning, caution, and important.

Tip With Caption

This is an example of a callout with a caption.

For your reference, here’s an example of a Python code cell in Quarto, along with a figure that gets generated, along with a caption and a label so that it can be referred to automatically as “Figure 1” (or whatever) in the writeup.

For a demonstration of a line plot on a polar axis, see Figure 11.

Code

import numpy as np
import matplotlib.pyplot as plt
r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r
fig, ax = plt.subplots(
  subplot_kw = {'projection': 'polar'} 
)
ax.plot(theta, r)
ax.set_rticks([0.5, 1, 1.5, 2])
ax.grid(True)
plt.show()

Figure 11: A line plot on a polar axis

Here’s an example of citing a source (see Phillips 1999, 33–35). Be sure the source information is entered in “BibTeX” form in the references.bib file.

The bibliography will automatically get generated. Any sources you cite in the document will be included. Other entries in the .bib file will not be included.

References

Phillips, T. P. 1999. “Possible Influence of the Magnetosphere on American History.” J. Oddball Res. 98: 1000–1003.